2350 lines
160 KiB
JSON
2350 lines
160 KiB
JSON
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{
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"description": "Get chat inputs from the Playground.",
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"display_name": "Chat Input",
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"id": "ChatInput-bqH7H",
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"field_order": [
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"input_value",
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"sender_name",
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"session_id",
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],
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"metadata": {
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"code_hash": "f701f686b325",
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"dependencies": {
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"dependencies": [
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{
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}
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],
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},
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},
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{
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"display_name": "Chat Message",
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"method": "message_response",
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],
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}
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],
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n temp_file=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n # Ensure files is a list and filter out empty/None values\n files = self.files if self.files else []\n if files and not isinstance(files, list):\n files = [files]\n # Filter out None/empty values\n files = [f for f in files if f is not None and f != \"\"]\n\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=files,\n )\n if self.session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n"
|
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},
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"files": {
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"_input_type": "FileInput",
|
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"advanced": true,
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"display_name": "Files",
|
|
"dynamic": false,
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"fileTypes": [
|
|
"csv",
|
|
"json",
|
|
"pdf",
|
|
"txt",
|
|
"md",
|
|
"mdx",
|
|
"yaml",
|
|
"yml",
|
|
"xml",
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"html",
|
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"htm",
|
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"docx",
|
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"py",
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"sh",
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"sql",
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"js",
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"ts",
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"tsx",
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"jpg",
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"jpeg",
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"png",
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"bmp",
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"image"
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],
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"file_path": "",
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"info": "Files to be sent with the message.",
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"list": true,
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"list_add_label": "Add More",
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"name": "files",
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"placeholder": "",
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"required": false,
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"show": true,
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"temp_file": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "file",
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"value": ""
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},
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"input_value": {
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"_input_type": "MultilineInput",
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"advanced": false,
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"copy_field": false,
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"display_name": "Input Text",
|
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"dynamic": false,
|
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"info": "Message to be passed as input.",
|
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"input_types": [],
|
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"list": false,
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"list_add_label": "Add More",
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"load_from_db": false,
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"multiline": true,
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"name": "input_value",
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"placeholder": "",
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"required": false,
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"show": true,
|
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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},
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"sender": {
|
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"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Type of sender.",
|
|
"name": "sender",
|
|
"options": [
|
|
"Machine",
|
|
"User"
|
|
],
|
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"options_metadata": [],
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
|
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"toggle": false,
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"tool_mode": false,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
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"value": "User"
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},
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"sender_name": {
|
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"_input_type": "MessageTextInput",
|
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"advanced": true,
|
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"display_name": "Sender Name",
|
|
"dynamic": false,
|
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"info": "Name of the sender.",
|
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"input_types": [
|
|
"Message"
|
|
],
|
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"list": false,
|
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"list_add_label": "Add More",
|
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"load_from_db": false,
|
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"name": "sender_name",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": "User"
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},
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"session_id": {
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"_input_type": "MessageTextInput",
|
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"advanced": true,
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"display_name": "Session ID",
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"dynamic": false,
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"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
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"input_types": [
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"Message"
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],
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"list": false,
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"list_add_label": "Add More",
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"load_from_db": false,
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"name": "session_id",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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},
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"should_store_message": {
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"_input_type": "BoolInput",
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"advanced": true,
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"display_name": "Store Messages",
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"dynamic": false,
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"info": "Store the message in the history.",
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"list": false,
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"list_add_label": "Add More",
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"name": "should_store_message",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "bool",
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"value": true
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}
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},
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"tool_mode": false
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},
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"selected_output": "message",
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"showNode": false,
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"type": "ChatInput"
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},
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"dragging": false,
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"id": "ChatInput-bqH7H",
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"measured": {
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"height": 48,
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"width": 192
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},
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"position": {
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"x": 1241.018940999428,
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"y": 1088.6203941617916
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},
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"selected": false,
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"type": "genericNode"
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},
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{
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"data": {
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"description": "Display a chat message in the Playground.",
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"display_name": "Chat Output",
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"id": "ChatOutput-BMVN5",
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"node": {
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"base_classes": [
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"Message"
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],
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Display a chat message in the Playground.",
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"display_name": "Chat Output",
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"documentation": "https://docs.langflow.org/components-io#chat-output",
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"edited": false,
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"field_order": [
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"input_value",
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"should_store_message",
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"sender",
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"sender_name",
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"session_id",
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"data_template",
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"clean_data"
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],
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"frozen": false,
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"icon": "MessagesSquare",
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"metadata": {
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"code_hash": "9647f4d2f4b4",
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"dependencies": {
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"dependencies": [
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{
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"name": "orjson",
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},
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{
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"name": "fastapi",
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"version": "0.117.1"
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},
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{
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"name": "lfx",
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"version": null
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],
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"total_dependencies": 3
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},
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"module": "custom_components.chat_output"
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},
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"outputs": [
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Output Message",
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"group_outputs": false,
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"method": "message_response",
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"name": "message",
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"options": null,
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"required_inputs": null,
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"selected": "Message",
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"tool_mode": true,
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"types": [
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"Message"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"clean_data": {
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"_input_type": "BoolInput",
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"advanced": true,
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"display_name": "Basic Clean Data",
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"dynamic": false,
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|
"info": "Whether to clean data before converting to string.",
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"list": false,
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"list_add_label": "Add More",
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"name": "clean_data",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "bool",
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"value": true
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n advanced=True,\n info=\"Whether to clean data before converting to string.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, _, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n clean_data: bool = getattr(self, \"clean_data\", False)\n return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n"
|
|
},
|
|
"data_template": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Data Template",
|
|
"dynamic": false,
|
|
"info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "data_template",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "{text}"
|
|
},
|
|
"input_value": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Inputs",
|
|
"dynamic": false,
|
|
"info": "Message to be passed as output.",
|
|
"input_types": [
|
|
"Data",
|
|
"DataFrame",
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "input_value",
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"sender": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Type of sender.",
|
|
"name": "sender",
|
|
"options": [
|
|
"Machine",
|
|
"User"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "Machine"
|
|
},
|
|
"sender_name": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Sender Name",
|
|
"dynamic": false,
|
|
"info": "Name of the sender.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "sender_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "AI"
|
|
},
|
|
"session_id": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Session ID",
|
|
"dynamic": false,
|
|
"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "session_id",
|
|
"placeholder": "",
|
|
"required": false,
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|
"show": true,
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|
"title_case": false,
|
|
"tool_mode": false,
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|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"should_store_message": {
|
|
"_input_type": "BoolInput",
|
|
"advanced": true,
|
|
"display_name": "Store Messages",
|
|
"dynamic": false,
|
|
"info": "Store the message in the history.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "should_store_message",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "bool",
|
|
"value": true
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": false,
|
|
"type": "ChatOutput"
|
|
},
|
|
"dragging": false,
|
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"id": "ChatOutput-BMVN5",
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"measured": {
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"height": 48,
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"width": 192
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},
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"position": {
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"x": 2642.8863560882705,
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"y": 740.3042509819792
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},
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"selected": false,
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"type": "genericNode"
|
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},
|
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{
|
|
"data": {
|
|
"id": "OpenSearch-iYfjf",
|
|
"node": {
|
|
"base_classes": [
|
|
"Data",
|
|
"DataFrame",
|
|
"VectorStore"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
|
|
"display_name": "OpenSearch",
|
|
"documentation": "",
|
|
"edited": true,
|
|
"field_order": [
|
|
"docs_metadata",
|
|
"opensearch_url",
|
|
"index_name",
|
|
"engine",
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|
"space_type",
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|
"ef_construction",
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|
"m",
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"ingest_data",
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"search_query",
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"should_cache_vector_store",
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"embedding",
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"vector_field",
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"number_of_results",
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"filter_expression",
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"auth_mode",
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"username",
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"password",
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"jwt_token",
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"jwt_header",
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"bearer_prefix",
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"use_ssl",
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"verify_certs"
|
|
],
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"frozen": false,
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"icon": "OpenSearch",
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"legacy": false,
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"metadata": {
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"code_hash": "c81b23acb81a",
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"dependencies": {
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"dependencies": [
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{
|
|
"name": "opensearchpy",
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"version": "2.8.0"
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|
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{
|
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"name": "lfx",
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"version": null
|
|
}
|
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],
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"total_dependencies": 2
|
|
},
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|
"module": "custom_components.opensearch"
|
|
},
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|
"minimized": false,
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|
"output_types": [],
|
|
"outputs": [
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|
{
|
|
"allows_loop": false,
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|
"cache": true,
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"display_name": "Search Results",
|
|
"group_outputs": false,
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"hidden": null,
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|
"method": "search_documents",
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|
"name": "search_results",
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"options": null,
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"required_inputs": null,
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"selected": "Data",
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"tool_mode": true,
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"types": [
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|
],
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"value": "__UNDEFINED__"
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},
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{
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"method": "as_dataframe",
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"name": "dataframe",
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"selected": "DataFrame",
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],
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"value": "__UNDEFINED__"
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},
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{
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"allows_loop": false,
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"display_name": "Vector Store Connection",
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"group_outputs": false,
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"name": "vectorstoreconnection",
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],
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|
}
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],
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"pinned": false,
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"template": {
|
|
"_type": "Component",
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"auth_mode": {
|
|
"_input_type": "DropdownInput",
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"advanced": false,
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|
"combobox": false,
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"dialog_inputs": {},
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"display_name": "Authentication Mode",
|
|
"dynamic": false,
|
|
"external_options": {},
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"info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
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"load_from_db": false,
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"name": "auth_mode",
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"options": [
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"real_time_refresh": true,
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"value": "jwt"
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},
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"bearer_prefix": {
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"_input_type": "BoolInput",
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"advanced": true,
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"display_name": "Prefix 'Bearer '",
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"info": "",
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"list": false,
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"list_add_label": "Add More",
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"name": "bearer_prefix",
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"placeholder": "",
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"required": false,
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"show": false,
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"trace_as_metadata": true,
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"type": "bool",
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"value": true
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
|
|
"required": true,
|
|
"show": true,
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"title_case": false,
|
|
"type": "code",
|
|
"value": "from __future__ import annotations\n\nimport copy\nimport json\nimport uuid\nfrom typing import Any\n\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nfrom opensearchpy import OpenSearch, helpers\nfrom opensearchpy.exceptions import RequestError\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\ndef normalize_model_name(model_name: str) -> str:\n \"\"\"Normalize embedding model name for use as field suffix.\n\n Converts model names to valid OpenSearch field names by replacing\n special characters and ensuring alphanumeric format.\n\n Args:\n model_name: Original embedding model name (e.g., \"text-embedding-3-small\")\n\n Returns:\n Normalized field suffix (e.g., \"text_embedding_3_small\")\n \"\"\"\n normalized = model_name.lower()\n # Replace common separators with underscores\n normalized = normalized.replace(\"-\", \"_\").replace(\":\", \"_\").replace(\"/\", \"_\").replace(\".\", \"_\")\n # Remove any non-alphanumeric characters except underscores\n normalized = \"\".join(c if c.isalnum() or c == \"_\" else \"_\" for c in normalized)\n # Remove duplicate underscores\n while \"__\" in normalized:\n normalized = normalized.replace(\"__\", \"_\")\n return normalized.strip(\"_\")\n\n\ndef get_embedding_field_name(model_name: str) -> str:\n \"\"\"Get the dynamic embedding field name for a model.\n\n Args:\n model_name: Embedding model name\n\n Returns:\n Field name in format: chunk_embedding_{normalized_model_name}\n \"\"\"\n return f\"chunk_embedding_{normalize_model_name(model_name)}\"\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch Vector Store Component with Multi-Model Hybrid Search Capabilities.\n\n This component provides vector storage and retrieval using OpenSearch, combining semantic\n similarity search (KNN) with keyword-based search for optimal results. It supports:\n - Multiple embedding models per index with dynamic field names\n - Automatic detection and querying of all available embedding models\n - Parallel embedding generation for multi-model search\n - Document ingestion with model tracking\n - Advanced filtering and aggregations\n - Flexible authentication options\n\n Features:\n - Multi-model vector storage with dynamic fields (chunk_embedding_{model_name})\n - Hybrid search combining multiple KNN queries (dis_max) + keyword matching\n - Auto-detection of available models in the index\n - Parallel query embedding generation for all detected models\n - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n - Flexible authentication (Basic auth, JWT tokens)\n \"\"\"\n\n display_name: str = \"OpenSearch (Multi-Model)\"\n icon: str = \"OpenSearch\"\n description: str = (\n \"Store and search documents using OpenSearch with multi-model hybrid semantic and keyword search.\"\n )\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[i.name for i in LCVectorStoreComponent.inputs], # search_query, add_documents, etc.\n \"embedding\",\n \"embedding_model_name\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n \"engine\",\n \"space_type\",\n \"ef_construction\",\n \"m\",\n \"num_candidates\",\n \"docs_metadata\",\n ]\n\n inputs = [\n TableInput(\n name=\"docs_metadata\",\n display_name=\"Document Metadata\",\n info=(\n \"Additional metadata key-value pairs to be added to all ingested documents. \"\n \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n ),\n table_schema=[\n {\n \"name\": \"key\",\n \"display_name\": \"Key\",\n \"type\": \"str\",\n \"description\": \"Key name\",\n },\n {\n \"name\": \"value\",\n \"display_name\": \"Value\",\n \"type\": \"str\",\n \"description\": \"Value of the metadata\",\n },\n ],\n value=[],\n input_types=[\"Data\"]\n ),\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=(\n \"The connection URL for your OpenSearch cluster \"\n \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n ),\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=(\n \"The OpenSearch index name where documents will be stored and searched. \"\n \"Will be created automatically if it doesn't exist.\"\n ),\n ),\n DropdownInput(\n name=\"engine\",\n display_name=\"Vector Engine\",\n options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n value=\"jvector\",\n info=(\n \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n ),\n advanced=True,\n ),\n DropdownInput(\n name=\"space_type\",\n display_name=\"Distance Metric\",\n options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n value=\"l2\",\n info=(\n \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n ),\n advanced=True,\n ),\n IntInput(\n name=\"ef_construction\",\n display_name=\"EF Construction\",\n value=512,\n info=(\n \"Size of the dynamic candidate list during index construction. \"\n \"Higher values improve recall but increase indexing time and memory usage.\"\n ),\n advanced=True,\n ),\n IntInput(\n name=\"m\",\n display_name=\"M Parameter\",\n value=16,\n info=(\n \"Number of bidirectional connections for each vector in the HNSW graph. \"\n \"Higher values improve search quality but increase memory usage and indexing time.\"\n ),\n advanced=True,\n ),\n IntInput(\n name=\"num_candidates\",\n display_name=\"Candidate Pool Size\",\n value=1000,\n info=(\n \"Number of approximate neighbors to consider for each KNN query. \"\n \"Some OpenSearch deployments do not support this parameter; set to 0 to disable.\"\n ),\n advanced=True,\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n StrInput(\n name=\"embedding_model_name\",\n display_name=\"Embedding Model Name\",\n value=\"\",\n info=(\n \"Name of the embedding model being used (e.g., 'text-embedding-3-small'). \"\n \"Used to create dynamic vector field names and track which model embedded each document. \"\n \"Auto-detected from embedding component if not specified.\"\n ),\n ),\n StrInput(\n name=\"vector_field\",\n display_name=\"Legacy Vector Field Name\",\n value=\"chunk_embedding\",\n advanced=True,\n info=(\n \"Legacy field name for backward compatibility. New documents use dynamic fields \"\n \"(chunk_embedding_{model_name}) based on the embedding_model_name.\"\n ),\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Result Limit\",\n value=10,\n advanced=True,\n info=(\n \"Default maximum number of search results to return when no limit is \"\n \"specified in the filter expression.\"\n ),\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Search Filters (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n \"Format 1 - Explicit filters:\\n\"\n '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n \"Format 2 - Context-style mapping:\\n\"\n '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n ),\n ),\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Authentication Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=(\n \"Authentication method: 'basic' for username/password authentication, \"\n \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n ),\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"OpenSearch Password\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"JWT\",\n load_from_db=False,\n show=True,\n info=(\n \"Valid JSON Web Token for authentication. \"\n \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n ),\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n # ----- TLS -----\n BoolInput(\n name=\"use_ssl\",\n display_name=\"Use SSL/TLS\",\n value=True,\n advanced=True,\n info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n ),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify SSL Certificates\",\n value=False,\n advanced=True,\n info=(\n \"Verify SSL certificates when connecting. \"\n \"Disable for self-signed certificates in development environments.\"\n ),\n ),\n ]\n\n def _get_embedding_model_name(self) -> str:\n \"\"\"Get the embedding model name from component config or embedding object.\n\n Returns:\n Embedding model name\n\n Raises:\n ValueError: If embedding model name cannot be determined\n \"\"\"\n # First try explicit embedding_model_name input\n if hasattr(self, \"embedding_model_name\") and self.embedding_model_name:\n return self.embedding_model_name.strip()\n\n # Try to get from embedding component\n if hasattr(self, \"embedding\") and self.embedding:\n if hasattr(self.embedding, \"model\"):\n return str(self.embedding.model)\n if hasattr(self.embedding, \"model_name\"):\n return str(self.embedding.model_name)\n\n msg = (\n \"Could not determine embedding model name. \"\n \"Please set the 'embedding_model_name' field or ensure the embedding component \"\n \"has a 'model' or 'model_name' attribute.\"\n )\n raise ValueError(msg)\n\n # ---------- helper functions for index management ----------\n def _default_text_mapping(\n self,\n dim: int,\n engine: str = \"jvector\",\n space_type: str = \"l2\",\n ef_search: int = 512,\n ef_construction: int = 100,\n m: int = 16,\n vector_field: str = \"vector_field\",\n ) -> dict[str, Any]:\n \"\"\"Create the default OpenSearch index mapping for vector search.\n\n This method generates the index configuration with k-NN settings optimized\n for approximate nearest neighbor search using the specified vector engine.\n Includes the embedding_model keyword field for tracking which model was used.\n\n Args:\n dim: Dimensionality of the vector embeddings\n engine: Vector search engine (jvector, nmslib, faiss, lucene)\n space_type: Distance metric for similarity calculation\n ef_search: Size of dynamic list used during search\n ef_construction: Size of dynamic list used during index construction\n m: Number of bidirectional links for each vector\n vector_field: Name of the field storing vector embeddings\n\n Returns:\n Dictionary containing OpenSearch index mapping configuration\n \"\"\"\n return {\n \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n \"mappings\": {\n \"properties\": {\n vector_field: {\n \"type\": \"knn_vector\",\n \"dimension\": dim,\n \"method\": {\n \"name\": \"disk_ann\",\n \"space_type\": space_type,\n \"engine\": engine,\n \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n },\n },\n \"embedding_model\": {\"type\": \"keyword\"}, # Track which model was used\n \"embedding_dimensions\": {\"type\": \"integer\"},\n }\n },\n }\n\n def _ensure_embedding_field_mapping(\n self,\n client: OpenSearch,\n index_name: str,\n field_name: str,\n dim: int,\n engine: str,\n space_type: str,\n ef_construction: int,\n m: int,\n ) -> None:\n \"\"\"Lazily add a dynamic embedding field to the index if it doesn't exist.\n\n This allows adding new embedding models without recreating the entire index.\n Also ensures the embedding_model tracking field exists.\n\n Args:\n client: OpenSearch client instance\n index_name: Target index name\n field_name: Dynamic field name for this embedding model\n dim: Vector dimensionality\n engine: Vector search engine\n space_type: Distance metric\n ef_construction: Construction parameter\n m: HNSW parameter\n \"\"\"\n try:\n mapping = {\n \"properties\": {\n field_name: {\n \"type\": \"knn_vector\",\n \"dimension\": dim,\n \"method\": {\n \"name\": \"disk_ann\",\n \"space_type\": space_type,\n \"engine\": engine,\n \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n },\n },\n # Also ensure the embedding_model tracking field exists as keyword\n \"embedding_model\": {\n \"type\": \"keyword\"\n },\n \"embedding_dimensions\": {\n \"type\": \"integer\"\n }\n }\n }\n client.indices.put_mapping(index=index_name, body=mapping)\n logger.info(f\"Added/updated embedding field mapping: {field_name}\")\n except Exception as e:\n logger.warning(f\"Could not add embedding field mapping for {field_name}: {e}\")\n\n def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n Amazon OpenSearch Serverless has restrictions on which vector engines\n can be used. This method ensures the selected engine is compatible.\n\n Args:\n is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n engine: The selected vector search engine\n\n Raises:\n ValueError: If AOSS is used with an incompatible engine\n \"\"\"\n if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n raise ValueError(msg)\n\n def _is_aoss_enabled(self, http_auth: Any) -> bool:\n \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n Args:\n http_auth: The HTTP authentication object\n\n Returns:\n True if AOSS is enabled, False otherwise\n \"\"\"\n return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n def _bulk_ingest_embeddings(\n self,\n client: OpenSearch,\n index_name: str,\n embeddings: list[list[float]],\n texts: list[str],\n metadatas: list[dict] | None = None,\n ids: list[str] | None = None,\n vector_field: str = \"vector_field\",\n text_field: str = \"text\",\n embedding_model: str = \"unknown\",\n mapping: dict | None = None,\n max_chunk_bytes: int | None = 1 * 1024 * 1024,\n *,\n is_aoss: bool = False,\n ) -> list[str]:\n \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n This method uses bulk operations to insert documents with their vector\n embeddings and metadata into the specified OpenSearch index. Each document\n is tagged with the embedding_model name for tracking.\n\n Args:\n client: OpenSearch client instance\n index_name: Target index for document storage\n embeddings: List of vector embeddings for each document\n texts: List of document texts\n metadatas: Optional metadata dictionaries for each document\n ids: Optional document IDs (UUIDs generated if not provided)\n vector_field: Field name for storing vector embeddings\n text_field: Field name for storing document text\n embedding_model: Name of the embedding model used\n mapping: Optional index mapping configuration\n max_chunk_bytes: Maximum size per bulk request chunk\n is_aoss: Whether using Amazon OpenSearch Serverless\n\n Returns:\n List of document IDs that were successfully ingested\n \"\"\"\n if not mapping:\n mapping = {}\n\n requests = []\n return_ids = []\n vector_dimensions = len(embeddings[0]) if embeddings else None\n\n for i, text in enumerate(texts):\n metadata = metadatas[i] if metadatas else {}\n if vector_dimensions is not None and \"embedding_dimensions\" not in metadata:\n metadata = {**metadata, \"embedding_dimensions\": vector_dimensions}\n _id = ids[i] if ids else str(uuid.uuid4())\n request = {\n \"_op_type\": \"index\",\n \"_index\": index_name,\n vector_field: embeddings[i],\n text_field: text,\n \"embedding_model\": embedding_model, # Track which model was used\n **metadata,\n }\n if is_aoss:\n request[\"id\"] = _id\n else:\n request[\"_id\"] = _id\n requests.append(request)\n return_ids.append(_id)\n if metadatas:\n self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n return return_ids\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> dict[str, Any]:\n \"\"\"Build authentication configuration for OpenSearch client.\n\n Constructs the appropriate authentication parameters based on the\n selected auth mode (basic username/password or JWT token).\n\n Returns:\n Dictionary containing authentication configuration\n\n Raises:\n ValueError: If required authentication parameters are missing\n \"\"\"\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n raise ValueError(msg)\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n msg = \"Auth Mode is 'basic' but username/password are missing.\"\n raise ValueError(msg)\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n \"\"\"Create and configure an OpenSearch client instance.\n\n Returns:\n Configured OpenSearch client ready for operations\n \"\"\"\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our \"vector store.\"\n self.log(self.ingest_data)\n client = self.build_client()\n self._add_documents_to_vector_store(client=client)\n return client\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n This method handles the complete document ingestion pipeline:\n - Prepares document data and metadata\n - Generates vector embeddings\n - Creates appropriate index mappings with dynamic field names\n - Bulk inserts documents with vectors and model tracking\n\n Args:\n client: OpenSearch client for performing operations\n \"\"\"\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n docs = self.ingest_data or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n # Get embedding model name\n embedding_model = self._get_embedding_model_name()\n dynamic_field_name = get_embedding_field_name(embedding_model)\n\n self.log(f\"Using embedding model: {embedding_model}\")\n self.log(f\"Dynamic vector field: {dynamic_field_name}\")\n\n # Extract texts and metadata from documents\n texts = []\n metadatas = []\n # Process docs_metadata table input into a dict\n additional_metadata = {}\n if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n logger.info(f\"[LF] Docs metadata {self.docs_metadata}\")\n if isinstance(self.docs_metadata[-1], Data):\n logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n self.docs_metadata = self.docs_metadata[-1].data\n logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n additional_metadata.update(self.docs_metadata)\n else:\n for item in self.docs_metadata:\n if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n additional_metadata[item[\"key\"]] = item[\"value\"]\n # Replace string \"None\" values with actual None\n for key, value in additional_metadata.items():\n if value == \"None\":\n additional_metadata[key] = None\n logger.info(f\"[LF] Additional metadata {additional_metadata}\")\n for doc_obj in docs:\n data_copy = json.loads(doc_obj.model_dump_json())\n text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n texts.append(text)\n\n # Merge additional metadata from table input\n data_copy.update(additional_metadata)\n\n metadatas.append(data_copy)\n self.log(metadatas)\n if not self.embedding:\n msg = \"Embedding handle is required to embed documents.\"\n raise ValueError(msg)\n\n # Generate embeddings (threaded for concurrency)\n def embed_chunk(chunk_text: str) -> list[float]:\n return self.embedding.embed_documents([chunk_text])[0]\n\n try:\n max_workers = min(max(len(texts), 1), 8)\n with ThreadPoolExecutor(max_workers=max_workers) as executor:\n futures = {executor.submit(embed_chunk, chunk): idx for idx, chunk in enumerate(texts)}\n vectors = [None] * len(texts)\n for future in as_completed(futures):\n idx = futures[future]\n vectors[idx] = future.result()\n except Exception as exc:\n logger.warning(\n \"Threaded embedding generation failed, falling back to synchronous mode: %s\",\n exc,\n )\n vectors = self.embedding.embed_documents(texts)\n\n if not vectors:\n self.log(\"No vectors generated from documents.\")\n return\n\n # Get vector dimension for mapping\n dim = len(vectors[0]) if vectors else 768 # default fallback\n\n # Check for AOSS\n auth_kwargs = self._build_auth_kwargs()\n is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n # Validate engine with AOSS\n engine = getattr(self, \"engine\", \"jvector\")\n self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n # Create mapping with proper KNN settings\n space_type = getattr(self, \"space_type\", \"l2\")\n ef_construction = getattr(self, \"ef_construction\", 512)\n m = getattr(self, \"m\", 16)\n\n mapping = self._default_text_mapping(\n dim=dim,\n engine=engine,\n space_type=space_type,\n ef_construction=ef_construction,\n m=m,\n vector_field=dynamic_field_name, # Use dynamic field name\n )\n\n # Ensure index exists with baseline mapping\n try:\n if not client.indices.exists(index=self.index_name):\n self.log(f\"Creating index '{self.index_name}' with base mapping\")\n client.indices.create(index=self.index_name, body=mapping)\n except RequestError as creation_error:\n if creation_error.error != \"resource_already_exists_exception\":\n logger.warning(\n f\"Failed to create index '{self.index_name}': {creation_error}\"\n )\n\n # Ensure the dynamic field exists in the index\n self._ensure_embedding_field_mapping(\n client=client,\n index_name=self.index_name,\n field_name=dynamic_field_name,\n dim=dim,\n engine=engine,\n space_type=space_type,\n ef_construction=ef_construction,\n m=m,\n )\n\n self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with model '{embedding_model}'...\")\n\n # Use the bulk ingestion with model tracking\n return_ids = self._bulk_ingest_embeddings(\n client=client,\n index_name=self.index_name,\n embeddings=vectors,\n texts=texts,\n metadatas=metadatas,\n vector_field=dynamic_field_name, # Use dynamic field name\n text_field=\"text\",\n embedding_model=embedding_model, # Track the model\n mapping=mapping,\n is_aoss=is_aoss,\n )\n self.log(metadatas)\n\n self.log(f\"Successfully indexed {len(return_ids)} documents with model {embedding_model}.\")\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n This method accepts two filter formats and converts them to standardized\n OpenSearch query clauses:\n\n Format A - Explicit filters:\n {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n \"limit\": 10, \"score_threshold\": 1.5}\n\n Format B - Context-style mapping:\n {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n Args:\n filter_obj: Filter configuration dictionary or None\n\n Returns:\n List of OpenSearch filter clauses (term/terms objects)\n Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n \"\"\"\n if not filter_obj:\n return []\n\n # If it is a string, try to parse it once\n if isinstance(filter_obj, str):\n try:\n filter_obj = json.loads(filter_obj)\n except json.JSONDecodeError:\n # Not valid JSON - treat as no filters\n return []\n\n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n explicit_clauses: list[dict] = []\n for f in raw or []:\n if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n explicit_clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n explicit_clauses.append(f)\n return explicit_clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\n \"data_sources\": \"filename\",\n \"document_types\": \"mimetype\",\n \"owners\": \"owner\",\n }\n context_clauses: list[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n context_clauses.append({\"term\": {field: values[0]}})\n else:\n context_clauses.append({\"terms\": {field: values}})\n return context_clauses\n\n def _detect_available_models(self, client: OpenSearch, filter_clauses: list[dict] = None) -> list[str]:\n \"\"\"Detect which embedding models have documents in the index.\n\n Uses aggregation to find all unique embedding_model values, optionally\n filtered to only documents matching the user's filter criteria.\n\n Args:\n client: OpenSearch client instance\n filter_clauses: Optional filter clauses to scope model detection\n\n Returns:\n List of embedding model names found in the index\n \"\"\"\n try:\n agg_query = {\n \"size\": 0,\n \"aggs\": {\n \"embedding_models\": {\n \"terms\": {\n \"field\": \"embedding_model.keyword\",\n \"size\": 10\n }\n }\n }\n }\n\n # Apply filters to model detection if any exist\n if filter_clauses:\n agg_query[\"query\"] = {\n \"bool\": {\n \"filter\": filter_clauses\n }\n }\n\n result = client.search(index=self.index_name, body=agg_query)\n buckets = result.get(\"aggregations\", {}).get(\"embedding_models\", {}).get(\"buckets\", [])\n models = [b[\"key\"] for b in buckets if b[\"key\"]]\n\n logger.info(\n f\"Detected embedding models in corpus: {models}\"\n + (f\" (with {len(filter_clauses)} filters)\" if filter_clauses else \"\")\n )\n return models\n except Exception as e:\n logger.warning(f\"Failed to detect embedding models: {e}\")\n # Fallback to current model\n return [self._get_embedding_model_name()]\n\n def _get_index_properties(self, client: OpenSearch) -> dict[str, Any] | None:\n \"\"\"Retrieve flattened mapping properties for the current index.\"\"\"\n try:\n mapping = client.indices.get_mapping(index=self.index_name)\n except Exception as e:\n logger.warning(\n f\"Failed to fetch mapping for index '{self.index_name}': {e}. Proceeding without mapping metadata.\"\n )\n return None\n\n properties: dict[str, Any] = {}\n for index_data in mapping.values():\n props = index_data.get(\"mappings\", {}).get(\"properties\", {})\n if isinstance(props, dict):\n properties.update(props)\n return properties\n\n def _is_knn_vector_field(self, properties: dict[str, Any] | None, field_name: str) -> bool:\n \"\"\"Check whether the field is mapped as a knn_vector.\"\"\"\n if not field_name:\n return False\n if properties is None:\n logger.warning(\n f\"Mapping metadata unavailable; assuming field '{field_name}' is usable.\"\n )\n return True\n field_def = properties.get(field_name)\n if not isinstance(field_def, dict):\n return False\n if field_def.get(\"type\") == \"knn_vector\":\n return True\n\n nested_props = field_def.get(\"properties\")\n if isinstance(nested_props, dict) and nested_props.get(\"type\") == \"knn_vector\":\n return True\n\n return False\n\n # ---------- search (multi-model hybrid) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n \"\"\"Perform multi-model hybrid search combining multiple vector similarities and keyword matching.\n\n This method executes a sophisticated search that:\n 1. Auto-detects all embedding models present in the index\n 2. Generates query embeddings for ALL detected models in parallel\n 3. Combines multiple KNN queries using dis_max (picks best match)\n 4. Adds keyword search with fuzzy matching (30% weight)\n 5. Applies optional filtering and score thresholds\n 6. Returns aggregations for faceted search\n\n Search weights:\n - Semantic search (dis_max across all models): 70%\n - Keyword search: 30%\n\n Args:\n query: Search query string (used for both vector embedding and keyword search)\n\n Returns:\n List of search results with page_content, metadata, and relevance scores\n\n Raises:\n ValueError: If embedding component is not provided or filter JSON is invalid\n \"\"\"\n logger.info(self.ingest_data)\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression\n filter_obj = None\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n filter_obj = json.loads(self.filter_expression)\n except json.JSONDecodeError as e:\n msg = f\"Invalid filter_expression JSON: {e}\"\n raise ValueError(msg) from e\n\n if not self.embedding:\n msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n raise ValueError(msg)\n\n # Build filter clauses first so we can use them in model detection\n filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n # Detect available embedding models in the index (scoped by filters)\n available_models = self._detect_available_models(client, filter_clauses)\n\n if not available_models:\n logger.warning(\"No embedding models found in index, using current model\")\n available_models = [self._get_embedding_model_name()]\n\n # Generate embeddings for ALL detected models in parallel\n query_embeddings = {}\n\n # Note: Langflow is synchronous, so we can't use true async here\n # But we log the intent for parallel processing\n logger.info(f\"Generating embeddings for {len(available_models)} models\")\n\n for model_name in available_models:\n try:\n # In a real async environment, these would run in parallel\n # For now, they run sequentially\n vec = self.embedding.embed_query(q)\n query_embeddings[model_name] = vec\n logger.info(f\"Generated embedding for model: {model_name}\")\n except Exception as e:\n logger.error(f\"Failed to generate embedding for {model_name}: {e}\")\n\n if not query_embeddings:\n msg = \"Failed to generate embeddings for any model\"\n raise ValueError(msg)\n\n index_properties = self._get_index_properties(client)\n legacy_vector_field = getattr(self, \"vector_field\", \"chunk_embedding\")\n\n # Build KNN queries for each model\n embedding_fields: list[str] = []\n knn_queries_with_candidates = []\n knn_queries_without_candidates = []\n\n raw_num_candidates = getattr(self, \"num_candidates\", 1000)\n try:\n num_candidates = int(raw_num_candidates) if raw_num_candidates is not None else 0\n except (TypeError, ValueError):\n num_candidates = 0\n use_num_candidates = num_candidates > 0\n\n for model_name, embedding_vector in query_embeddings.items():\n field_name = get_embedding_field_name(model_name)\n selected_field = field_name\n\n # Only use the expected dynamic field - no legacy fallback\n # This prevents dimension mismatches between models\n if not self._is_knn_vector_field(index_properties, selected_field):\n logger.warning(\n f\"Skipping model {model_name}: field '{field_name}' is not mapped as knn_vector. \"\n f\"Documents must be indexed with this embedding model before querying.\"\n )\n continue\n\n embedding_fields.append(selected_field)\n\n base_query = {\n \"knn\": {\n selected_field: {\n \"vector\": embedding_vector,\n \"k\": 50,\n }\n }\n }\n\n if use_num_candidates:\n query_with_candidates = copy.deepcopy(base_query)\n query_with_candidates[\"knn\"][selected_field][\"num_candidates\"] = num_candidates\n else:\n query_with_candidates = base_query\n\n knn_queries_with_candidates.append(query_with_candidates)\n knn_queries_without_candidates.append(base_query)\n\n if not knn_queries_with_candidates:\n # No valid fields found - this can happen when:\n # 1. Index is empty (no documents yet)\n # 2. Embedding model has changed and field doesn't exist yet\n # Return empty results instead of failing\n logger.warning(\n \"No valid knn_vector fields found for embedding models. \"\n \"This may indicate an empty index or missing field mappings. \"\n \"Returning empty search results.\"\n )\n return []\n\n # Build exists filter - document must have at least one embedding field\n exists_any_embedding = {\n \"bool\": {\n \"should\": [{\"exists\": {\"field\": f}} for f in set(embedding_fields)],\n \"minimum_should_match\": 1\n }\n }\n\n # Combine user filters with exists filter\n all_filters = [*filter_clauses, exists_any_embedding]\n\n # Get limit and score threshold\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n # Build multi-model hybrid query\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"dis_max\": {\n \"tie_breaker\": 0.0, # Take only the best match, no blending\n \"boost\": 0.7, # 70% weight for semantic search\n \"queries\": knn_queries_with_candidates\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3, # 30% weight for keyword search\n }\n },\n ],\n \"minimum_should_match\": 1,\n \"filter\": all_filters,\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n \"embedding_models\": {\"terms\": {\"field\": \"embedding_model.keyword\", \"size\": 10}},\n },\n \"_source\": [\n \"filename\",\n \"mimetype\",\n \"page\",\n \"text\",\n \"source_url\",\n \"owner\",\n \"embedding_model\",\n \"allowed_users\",\n \"allowed_groups\",\n ],\n \"size\": limit,\n }\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n body[\"min_score\"] = score_threshold\n\n logger.info(\n f\"Executing multi-model hybrid search with {len(knn_queries_with_candidates)} embedding models\"\n )\n\n try:\n resp = client.search(index=self.index_name, body=body)\n except RequestError as e:\n error_message = str(e)\n lowered = error_message.lower()\n if use_num_candidates and \"num_candidates\" in lowered:\n logger.warning(\n \"Retrying search without num_candidates parameter due to cluster capabilities\",\n error=error_message,\n )\n fallback_body = copy.deepcopy(body)\n try:\n fallback_body[\"query\"][\"bool\"][\"should\"][0][\"dis_max\"][\"queries\"] = knn_queries_without_candidates\n except (KeyError, IndexError, TypeError) as inner_err:\n raise e from inner_err\n resp = client.search(index=self.index_name, body=fallback_body)\n elif \"knn_vector\" in lowered or (\"field\" in lowered and \"knn\" in lowered):\n fallback_vector = next(iter(query_embeddings.values()), None)\n if fallback_vector is None:\n raise\n fallback_field = legacy_vector_field or \"chunk_embedding\"\n logger.warning(\n \"KNN search failed for dynamic fields; falling back to legacy field '%s'.\",\n fallback_field,\n )\n fallback_body = copy.deepcopy(body)\n fallback_body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n knn_fallback = {\n \"knn\": {\n fallback_field: {\n \"vector\": fallback_vector,\n \"k\": 50,\n }\n }\n }\n if use_num_candidates:\n knn_fallback[\"knn\"][fallback_field][\"num_candidates\"] = num_candidates\n fallback_body[\"query\"][\"bool\"][\"should\"][0][\"dis_max\"][\"queries\"] = [knn_fallback]\n resp = client.search(index=self.index_name, body=fallback_body)\n else:\n raise\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n\n logger.info(f\"Found {len(hits)} results\")\n\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n \"\"\"Search documents and return results as Data objects.\n\n This is the main interface method that performs the multi-model search using the\n configured search_query and returns results in Langflow's Data format.\n\n Returns:\n List of Data objects containing search results with text and metadata\n\n Raises:\n Exception: If search operation fails\n \"\"\"\n try:\n raw = self.search(self.search_query or \"\")\n return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n self.log(self.ingest_data)\n except Exception as e:\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Dynamically update component configuration based on field changes.\n\n This method handles real-time UI updates, particularly for authentication\n mode changes that show/hide relevant input fields.\n\n Args:\n build_config: Current component configuration\n field_value: New value for the changed field\n field_name: Name of the field that changed\n\n Returns:\n Updated build configuration with appropriate field visibility\n \"\"\"\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n except (KeyError, ValueError) as e:\n self.log(f\"update_build_config error: {e}\")\n\n return build_config\n"
|
|
},
|
|
"docs_metadata": {
|
|
"_input_type": "TableInput",
|
|
"advanced": false,
|
|
"display_name": "Document Metadata",
|
|
"dynamic": false,
|
|
"info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
|
|
"input_types": [
|
|
"Data"
|
|
],
|
|
"is_list": true,
|
|
"list_add_label": "Add More",
|
|
"name": "docs_metadata",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"table_icon": "Table",
|
|
"table_schema": [
|
|
{
|
|
"description": "Key name",
|
|
"display_name": "Key",
|
|
"formatter": "text",
|
|
"name": "key",
|
|
"type": "str"
|
|
},
|
|
{
|
|
"description": "Value of the metadata",
|
|
"display_name": "Value",
|
|
"formatter": "text",
|
|
"name": "value",
|
|
"type": "str"
|
|
}
|
|
],
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"trigger_icon": "Table",
|
|
"trigger_text": "Open table",
|
|
"type": "table",
|
|
"value": []
|
|
},
|
|
"ef_construction": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "EF Construction",
|
|
"dynamic": false,
|
|
"info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "ef_construction",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 512
|
|
},
|
|
"embedding": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Embedding",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Embeddings"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "embedding",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"engine": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": true,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Vector Engine",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
|
|
"name": "engine",
|
|
"options": [
|
|
"jvector",
|
|
"nmslib",
|
|
"faiss",
|
|
"lucene"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "jvector"
|
|
},
|
|
"filter_expression": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": false,
|
|
"copy_field": false,
|
|
"display_name": "Search Filters (JSON)",
|
|
"dynamic": false,
|
|
"info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "filter_expression",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"index_name": {
|
|
"_input_type": "StrInput",
|
|
"advanced": false,
|
|
"display_name": "Index Name",
|
|
"dynamic": false,
|
|
"info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "index_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "documents"
|
|
},
|
|
"ingest_data": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Ingest Data",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Data",
|
|
"DataFrame"
|
|
],
|
|
"list": true,
|
|
"list_add_label": "Add More",
|
|
"name": "ingest_data",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"jwt_header": {
|
|
"_input_type": "StrInput",
|
|
"advanced": true,
|
|
"display_name": "JWT Header Name",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "jwt_header",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": false,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "Authorization"
|
|
},
|
|
"jwt_token": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "JWT Token",
|
|
"dynamic": false,
|
|
"info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "jwt_token",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "JWT"
|
|
},
|
|
"m": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "M Parameter",
|
|
"dynamic": false,
|
|
"info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "m",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 16
|
|
},
|
|
"number_of_results": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Default Result Limit",
|
|
"dynamic": false,
|
|
"info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
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"info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
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"name": "space_type",
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"options": [
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"type": "bool",
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},
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"username": {
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"name": "username",
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"type": "str",
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"value": "admin"
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},
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"vector_field": {
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"_input_type": "StrInput",
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"advanced": true,
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"display_name": "Vector Field Name",
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"dynamic": false,
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"info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
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"list": false,
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"list_add_label": "Add More",
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"load_from_db": false,
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"name": "vector_field",
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"type": "str",
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"value": "chunk_embedding"
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},
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"verify_certs": {
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"info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
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"list": false,
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"name": "verify_certs",
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"selected_output": "dataframe",
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"showNode": true,
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"type": "OpenSearchVectorStoreComponent"
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},
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"dragging": false,
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"id": "OpenSearch-iYfjf",
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"measured": {
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"height": 822,
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{
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"data": {
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"id": "EmbeddingModel-eZ6bT",
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"node": {
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"base_classes": [
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],
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"beta": false,
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"custom_fields": {},
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"description": "Generate embeddings using a specified provider.",
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"display_name": "Embedding Model",
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"documentation": "https://docs.langflow.org/components-embedding-models",
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"edited": false,
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"model_kwargs"
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"icon": "binary",
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"last_updated": "2025-10-01T20:14:43.010Z",
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{
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"name": "langchain_openai",
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{
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"total_dependencies": 2
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},
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"module": "custom_components.embedding_model"
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},
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"output_types": [],
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"outputs": [
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Embedding Model",
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"group_outputs": false,
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"method": "build_embeddings",
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"name": "embeddings",
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"options": null,
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"api_base": {
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"_input_type": "MessageTextInput",
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|
"advanced": true,
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|
"display_name": "API Base URL",
|
|
"dynamic": false,
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|
"info": "Base URL for the API. Leave empty for default.",
|
|
"input_types": [
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|
"Message"
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],
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"list": false,
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|
"list_add_label": "Add More",
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"load_from_db": false,
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"name": "api_base",
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"placeholder": "",
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"required": false,
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},
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"api_key": {
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"_input_type": "SecretStrInput",
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"advanced": false,
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"display_name": "OpenAI API Key",
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"dynamic": false,
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"info": "Model Provider API key",
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"input_types": [],
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"load_from_db": true,
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"name": "api_key",
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"password": true,
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"placeholder": "",
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"real_time_refresh": true,
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|
"required": true,
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|
"show": true,
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|
"type": "str",
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|
"value": "OPENAI_API_KEY"
|
|
},
|
|
"chunk_size": {
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|
"_input_type": "IntInput",
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|
"advanced": true,
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|
"display_name": "Chunk Size",
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|
"info": "",
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"name": "chunk_size",
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|
"placeholder": "",
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|
"show": true,
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},
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"multiline": true,
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"password": false,
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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|
"value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}],\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\" and field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n return build_config\n"
|
|
},
|
|
"dimensions": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Dimensions",
|
|
"dynamic": false,
|
|
"info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "dimensions",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"max_retries": {
|
|
"_input_type": "IntInput",
|
|
"advanced": true,
|
|
"display_name": "Max Retries",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "max_retries",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "int",
|
|
"value": 3
|
|
},
|
|
"model": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Name",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the embedding model to use",
|
|
"name": "model",
|
|
"options": [
|
|
"text-embedding-3-small",
|
|
"text-embedding-3-large",
|
|
"text-embedding-ada-002"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "text-embedding-3-small"
|
|
},
|
|
"model_kwargs": {
|
|
"_input_type": "DictInput",
|
|
"advanced": true,
|
|
"display_name": "Model Kwargs",
|
|
"dynamic": false,
|
|
"info": "Additional keyword arguments to pass to the model.",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "model_kwargs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"type": "dict",
|
|
"value": {}
|
|
},
|
|
"provider": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Provider",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the embedding model provider",
|
|
"name": "provider",
|
|
"options": [
|
|
"OpenAI"
|
|
],
|
|
"options_metadata": [
|
|
{
|
|
"icon": "OpenAI"
|
|
}
|
|
],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"toggle": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "OpenAI"
|
|
},
|
|
"request_timeout": {
|
|
"_input_type": "FloatInput",
|
|
"advanced": true,
|
|
"display_name": "Request Timeout",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "request_timeout",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_metadata": true,
|
|
"type": "float",
|
|
"value": ""
|
|
},
|
|
"show_progress_bar": {
|
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"_input_type": "BoolInput",
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"show": true,
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{
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"data": {
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"id": "Prompt Template-Wo6kR",
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"node": {
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"base_classes": [
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"Message"
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],
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {
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"template": [
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"prompt",
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"docs"
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]
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},
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"description": "Create a prompt template with dynamic variables.",
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"display_name": "Prompt Template",
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"documentation": "https://docs.langflow.org/components-prompts",
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"edited": false,
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"field_order": [
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"template",
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"tool_placeholder"
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],
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"frozen": false,
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"icon": "braces",
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"legacy": false,
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"metadata": {
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"code_hash": "7382d03ce412",
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"dependencies": {
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"dependencies": [
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{
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"name": "lfx",
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"version": null
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}
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"total_dependencies": 1
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"module": "custom_components.prompt_template"
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},
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"minimized": false,
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"output_types": [],
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"outputs": [
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Prompt",
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"group_outputs": false,
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"hidden": null,
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"method": "build_prompt",
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"name": "prompt",
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"options": null,
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"required_inputs": null,
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"selected": "Message",
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"tool_mode": true,
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"types": [
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"Message"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"priority": 0,
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"template": {
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"_type": "Component",
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import MessageTextInput, Output, PromptInput\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"braces\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n priority = 0 # Set priority to 0 to make it appear first\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(self) -> Message:\n prompt = Message.from_template(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n )\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n # Kept it duplicated for backwards compatibility\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n"
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},
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"docs": {
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"advanced": false,
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"display_name": "docs",
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"dynamic": false,
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"field_type": "str",
|
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"fileTypes": [],
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"file_path": "",
|
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"info": "",
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"input_types": [
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"Message"
|
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],
|
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "docs",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
|
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},
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"prompt": {
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"advanced": false,
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"display_name": "prompt",
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"dynamic": false,
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"field_type": "str",
|
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"fileTypes": [],
|
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"file_path": "",
|
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"info": "",
|
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"input_types": [
|
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"Message"
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],
|
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "prompt",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
|
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},
|
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"template": {
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"_input_type": "PromptInput",
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"advanced": false,
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"display_name": "Template",
|
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"dynamic": false,
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"info": "",
|
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"list": false,
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"list_add_label": "Add More",
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"load_from_db": false,
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"name": "template",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_input": true,
|
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"type": "prompt",
|
|
"value": "You are generating prompt nudges to help a user explore a corpus.\n\nTask:\n1) Skim the documents to infer common themes, entities, or tasks.\n2) Propose exactly three concise, distinct prompt nudges that encourage useful next queries.\n3) If the chat history is provided, use it to generate new questions that the user might have, based on the llm's response to his previous query. DO NOT repeat user questions.\n4) Make the nudges concise, close to 40 characters.\n5) The nudges are questions or commands that the user can make to the chatbot, which will respond looking at the corpus.\n4) Return strings only, separating the nudges by a newline. Don't include quotation marks.\n5) If any error occured, return blank. This will be used in production, so don't ask for more info or confirm a info like you're talking to me still. If, for some reason, you can't provide the nudges, your job failed and you just return blank.\nRules: Be brief. No duplicates. No explanations outside the strings of the nudges. English only.\n\nExamples:\n Show me this quarter's top 10 deals\n Summarize recent client interactions\n Search OpenSearch for mentions of our competitors\n\nChat history:\n{prompt}\n\nDocuments:\n{docs}\n\n"
|
|
},
|
|
"tool_placeholder": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Tool Placeholder",
|
|
"dynamic": false,
|
|
"info": "A placeholder input for tool mode.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "tool_placeholder",
|
|
"placeholder": "",
|
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"required": false,
|
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"show": true,
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"title_case": false,
|
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"tool_mode": true,
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"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": true,
|
|
"type": "Prompt Template"
|
|
},
|
|
"dragging": false,
|
|
"id": "Prompt Template-Wo6kR",
|
|
"measured": {
|
|
"height": 449,
|
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"width": 320
|
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},
|
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"position": {
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"x": 1669.0365272581178,
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"y": 712.1086273287026
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},
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"selected": false,
|
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"type": "genericNode"
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},
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{
|
|
"data": {
|
|
"id": "ParserComponent-tZs7s",
|
|
"node": {
|
|
"base_classes": [
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Extracts text using a template.",
|
|
"display_name": "Parser",
|
|
"documentation": "https://docs.langflow.org/components-processing#parser",
|
|
"edited": false,
|
|
"field_order": [
|
|
"input_data",
|
|
"mode",
|
|
"pattern",
|
|
"sep"
|
|
],
|
|
"frozen": false,
|
|
"icon": "braces",
|
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"legacy": false,
|
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"metadata": {
|
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"code_hash": "bf19ee6feee3",
|
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"dependencies": {
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"dependencies": [
|
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{
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"name": "lfx",
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"version": null
|
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}
|
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],
|
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"total_dependencies": 1
|
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},
|
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"module": "custom_components.parser"
|
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},
|
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"minimized": false,
|
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"output_types": [],
|
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"outputs": [
|
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{
|
|
"allows_loop": false,
|
|
"cache": true,
|
|
"display_name": "Parsed Text",
|
|
"group_outputs": false,
|
|
"method": "parse_combined_text",
|
|
"name": "parsed_text",
|
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"options": null,
|
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"required_inputs": null,
|
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"selected": "Message",
|
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"tool_mode": true,
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"types": [
|
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"Message"
|
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],
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"value": "__UNDEFINED__"
|
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}
|
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],
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"pinned": false,
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"template": {
|
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"_type": "Component",
|
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"code": {
|
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"advanced": true,
|
|
"dynamic": true,
|
|
"fileTypes": [],
|
|
"file_path": "",
|
|
"info": "",
|
|
"list": false,
|
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"load_from_db": false,
|
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"multiline": true,
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"name": "code",
|
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"password": false,
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "code",
|
|
"value": "from lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, HandleInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\n\n\nclass ParserComponent(Component):\n display_name = \"Parser\"\n description = \"Extracts text using a template.\"\n documentation: str = \"https://docs.langflow.org/components-processing#parser\"\n icon = \"braces\"\n\n inputs = [\n HandleInput(\n name=\"input_data\",\n display_name=\"Data or DataFrame\",\n input_types=[\"DataFrame\", \"Data\"],\n info=\"Accepts either a DataFrame or a Data object.\",\n required=True,\n ),\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Parser\", \"Stringify\"],\n value=\"Parser\",\n info=\"Convert into raw string instead of using a template.\",\n real_time_refresh=True,\n ),\n MultilineInput(\n name=\"pattern\",\n display_name=\"Template\",\n info=(\n \"Use variables within curly brackets to extract column values for DataFrames \"\n \"or key values for Data.\"\n \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n ),\n value=\"Text: {text}\", # Example default\n dynamic=True,\n show=True,\n required=True,\n ),\n MessageTextInput(\n name=\"sep\",\n display_name=\"Separator\",\n advanced=True,\n value=\"\\n\",\n info=\"String used to separate rows/items.\",\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Parsed Text\",\n name=\"parsed_text\",\n info=\"Formatted text output.\",\n method=\"parse_combined_text\",\n ),\n ]\n\n def update_build_config(self, build_config, field_value, field_name=None):\n \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n if field_name == \"mode\":\n build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n if field_value:\n clean_data = BoolInput(\n name=\"clean_data\",\n display_name=\"Clean Data\",\n info=(\n \"Enable to clean the data by removing empty rows and lines \"\n \"in each cell of the DataFrame/ Data object.\"\n ),\n value=True,\n advanced=True,\n required=False,\n )\n build_config[\"clean_data\"] = clean_data.to_dict()\n else:\n build_config.pop(\"clean_data\", None)\n\n return build_config\n\n def _clean_args(self):\n \"\"\"Prepare arguments based on input type.\"\"\"\n input_data = self.input_data\n\n match input_data:\n case list() if all(isinstance(item, Data) for item in input_data):\n msg = \"List of Data objects is not supported.\"\n raise ValueError(msg)\n case DataFrame():\n return input_data, None\n case Data():\n return None, input_data\n case dict() if \"data\" in input_data:\n try:\n if \"columns\" in input_data: # Likely a DataFrame\n return DataFrame.from_dict(input_data), None\n # Likely a Data object\n return None, Data(**input_data)\n except (TypeError, ValueError, KeyError) as e:\n msg = f\"Invalid structured input provided: {e!s}\"\n raise ValueError(msg) from e\n case _:\n msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n raise ValueError(msg)\n\n def parse_combined_text(self) -> Message:\n \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n # Early return for stringify option\n if self.mode == \"Stringify\":\n return self.convert_to_string()\n\n df, data = self._clean_args()\n\n lines = []\n if df is not None:\n for _, row in df.iterrows():\n formatted_text = self.pattern.format(**row.to_dict())\n lines.append(formatted_text)\n elif data is not None:\n formatted_text = self.pattern.format(**data.data)\n lines.append(formatted_text)\n\n combined_text = self.sep.join(lines)\n self.status = combined_text\n return Message(text=combined_text)\n\n def convert_to_string(self) -> Message:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n result = \"\"\n if isinstance(self.input_data, list):\n result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n else:\n result = safe_convert(self.input_data or False)\n self.log(f\"Converted to string with length: {len(result)}\")\n\n message = Message(text=result)\n self.status = message\n return message\n"
|
|
},
|
|
"input_data": {
|
|
"_input_type": "HandleInput",
|
|
"advanced": false,
|
|
"display_name": "Data or DataFrame",
|
|
"dynamic": false,
|
|
"info": "Accepts either a DataFrame or a Data object.",
|
|
"input_types": [
|
|
"DataFrame",
|
|
"Data"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"name": "input_data",
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"mode": {
|
|
"_input_type": "TabInput",
|
|
"advanced": false,
|
|
"display_name": "Mode",
|
|
"dynamic": false,
|
|
"info": "Convert into raw string instead of using a template.",
|
|
"name": "mode",
|
|
"options": [
|
|
"Parser",
|
|
"Stringify"
|
|
],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
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"trace_as_metadata": true,
|
|
"type": "tab",
|
|
"value": "Parser"
|
|
},
|
|
"pattern": {
|
|
"_input_type": "MultilineInput",
|
|
"advanced": false,
|
|
"copy_field": false,
|
|
"display_name": "Template",
|
|
"dynamic": true,
|
|
"info": "Use variables within curly brackets to extract column values for DataFrames or key values for Data.For example: `Name: {Name}, Age: {Age}, Country: {Country}`",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "pattern",
|
|
"placeholder": "",
|
|
"required": true,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "{text}"
|
|
},
|
|
"sep": {
|
|
"_input_type": "MessageTextInput",
|
|
"advanced": true,
|
|
"display_name": "Separator",
|
|
"dynamic": false,
|
|
"info": "String used to separate rows/items.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "sep",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "\n"
|
|
}
|
|
},
|
|
"tool_mode": false
|
|
},
|
|
"showNode": true,
|
|
"type": "ParserComponent"
|
|
},
|
|
"dragging": false,
|
|
"id": "ParserComponent-tZs7s",
|
|
"measured": {
|
|
"height": 329,
|
|
"width": 320
|
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},
|
|
"position": {
|
|
"x": 1282.0613788430787,
|
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"y": 564.2200355777322
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},
|
|
"selected": false,
|
|
"type": "genericNode"
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "LanguageModelComponent-NSTA6",
|
|
"node": {
|
|
"base_classes": [
|
|
"LanguageModel",
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Runs a language model given a specified provider.",
|
|
"display_name": "Language Model",
|
|
"documentation": "https://docs.langflow.org/components-models",
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"edited": true,
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"field_order": [
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"provider",
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"model_name",
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"api_key",
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"input_value",
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"system_message",
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"stream",
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"temperature"
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],
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"frozen": false,
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"icon": "brain-circuit",
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"last_updated": "2025-10-01T20:14:43.010Z",
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"legacy": false,
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"metadata": {
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"code_hash": "bb5f8714781b",
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"dependencies": {
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"dependencies": [
|
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{
|
|
"name": "langchain_anthropic",
|
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"version": "0.3.14"
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},
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{
|
|
"name": "langchain_google_genai",
|
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"version": "2.0.6"
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},
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{
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"name": "langchain_openai",
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"version": "0.3.23"
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},
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{
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"name": "lfx",
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"version": null
|
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}
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],
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"total_dependencies": 4
|
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},
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"keywords": [
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"model",
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"llm",
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"language model",
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"large language model"
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],
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"module": "custom_components.language_model"
|
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},
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"minimized": false,
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"output_types": [],
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"outputs": [
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Model Response",
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"group_outputs": false,
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"hidden": null,
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"method": "text_response",
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"name": "text_output",
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"options": null,
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"required_inputs": null,
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"selected": "Message",
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"tool_mode": true,
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"types": [
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"Message"
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],
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"value": "__UNDEFINED__"
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},
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{
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"allows_loop": false,
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"cache": true,
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"display_name": "Language Model",
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"group_outputs": false,
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"hidden": null,
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"method": "build_model",
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"name": "model_output",
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"options": null,
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"required_inputs": null,
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"selected": "LanguageModel",
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"tool_mode": true,
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"types": [
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"LanguageModel"
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],
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"value": "__UNDEFINED__"
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}
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|
],
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"pinned": false,
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"priority": 0,
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"template": {
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"_type": "Component",
|
|
"api_key": {
|
|
"_input_type": "SecretStrInput",
|
|
"advanced": false,
|
|
"display_name": "OpenAI API Key",
|
|
"dynamic": false,
|
|
"info": "Model Provider API key",
|
|
"input_types": [],
|
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"load_from_db": true,
|
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"name": "api_key",
|
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"password": true,
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"placeholder": "",
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"real_time_refresh": true,
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
|
|
"value": "OPENAI_API_KEY"
|
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
|
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"value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom lfx.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom lfx.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom lfx.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n priority = 0 # Set priority to 0 to make it appear first\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n value=\"OpenAI\",\n info=\"Select the model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n value=OPENAI_CHAT_MODEL_NAMES[0],\n info=\"Select the model to use\",\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=False,\n show=True,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n provider = self.provider\n model_name = self.model_name\n temperature = self.temperature\n stream = self.stream\n\n if provider == \"OpenAI\":\n if not self.api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n\n if model_name in OPENAI_REASONING_MODEL_NAMES:\n # reasoning models do not support temperature (yet)\n temperature = None\n\n return ChatOpenAI(\n model_name=model_name,\n temperature=temperature,\n streaming=stream,\n openai_api_key=self.api_key,\n )\n if provider == \"Anthropic\":\n if not self.api_key:\n msg = \"Anthropic API key is required when using Anthropic provider\"\n raise ValueError(msg)\n return ChatAnthropic(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n anthropic_api_key=self.api_key,\n )\n if provider == \"Google\":\n if not self.api_key:\n msg = \"Google API key is required when using Google provider\"\n raise ValueError(msg)\n return ChatGoogleGenerativeAI(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n google_api_key=self.api_key,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n elif field_value == \"Anthropic\":\n build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n elif field_value == \"Google\":\n build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n # Hide system_message for o1 models - currently unsupported\n if \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = False\n elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = True\n return build_config\n"
|
|
},
|
|
"input_value": {
|
|
"_input_type": "MessageInput",
|
|
"advanced": false,
|
|
"display_name": "Input",
|
|
"dynamic": false,
|
|
"info": "The input text to send to the model",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"list_add_label": "Add More",
|
|
"load_from_db": false,
|
|
"name": "input_value",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"tool_mode": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"model_name": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Name",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the model to use",
|
|
"name": "model_name",
|
|
"options": [
|
|
"gpt-4o-mini",
|
|
"gpt-4o",
|
|
"gpt-4.1",
|
|
"gpt-4.1-mini",
|
|
"gpt-4.1-nano",
|
|
"gpt-4-turbo",
|
|
"gpt-4-turbo-preview",
|
|
"gpt-4",
|
|
"gpt-3.5-turbo",
|
|
"gpt-5",
|
|
"gpt-5-mini",
|
|
"gpt-5-nano",
|
|
"gpt-5-chat-latest",
|
|
"o1",
|
|
"o3-mini",
|
|
"o3",
|
|
"o3-pro",
|
|
"o4-mini",
|
|
"o4-mini-high"
|
|
],
|
|
"options_metadata": [],
|
|
"placeholder": "",
|
|
"real_time_refresh": true,
|
|
"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"toggle": false,
|
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"tool_mode": false,
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"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "gpt-4o-mini"
|
|
},
|
|
"provider": {
|
|
"_input_type": "DropdownInput",
|
|
"advanced": false,
|
|
"combobox": false,
|
|
"dialog_inputs": {},
|
|
"display_name": "Model Provider",
|
|
"dynamic": false,
|
|
"external_options": {},
|
|
"info": "Select the model provider",
|
|
"name": "provider",
|
|
"options": [
|
|
"OpenAI",
|
|
"Anthropic",
|
|
"Google"
|
|
],
|
|
"options_metadata": [
|
|
{
|
|
"icon": "OpenAI"
|
|
},
|
|
{
|
|
"icon": "Anthropic"
|
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},
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|
{
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|
"icon": "GoogleGenerativeAI"
|
|
}
|
|
],
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"placeholder": "",
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"real_time_refresh": true,
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"required": false,
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"show": true,
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"title_case": false,
|
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"toggle": false,
|
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "str",
|
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"value": "OpenAI"
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|
},
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|
"stream": {
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"_input_type": "BoolInput",
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"advanced": true,
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"display_name": "Stream",
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|
"dynamic": false,
|
|
"info": "Whether to stream the response",
|
|
"list": false,
|
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"list_add_label": "Add More",
|
|
"name": "stream",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"tool_mode": false,
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"trace_as_metadata": true,
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"type": "bool",
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"value": false
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},
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"system_message": {
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"_input_type": "MultilineInput",
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"advanced": false,
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"copy_field": false,
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"display_name": "System Message",
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"dynamic": false,
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"info": "A system message that helps set the behavior of the assistant",
|
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"input_types": [
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"Message"
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],
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"list": false,
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"list_add_label": "Add More",
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"load_from_db": false,
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"multiline": true,
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"name": "system_message",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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},
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"temperature": {
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"_input_type": "SliderInput",
|
|
"advanced": true,
|
|
"display_name": "Temperature",
|
|
"dynamic": false,
|
|
"info": "Controls randomness in responses",
|
|
"max_label": "",
|
|
"max_label_icon": "",
|
|
"min_label": "",
|
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"min_label_icon": "",
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"name": "temperature",
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"placeholder": "",
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"range_spec": {
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"max": 1,
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"min": 0,
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"step": 0.01,
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"step_type": "float"
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},
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"required": false,
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"show": true,
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"slider_buttons": false,
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"slider_buttons_options": [],
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"slider_input": false,
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"title_case": false,
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"tool_mode": false,
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"type": "slider",
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"value": 0.1
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}
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},
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"tool_mode": false
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},
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"selected_output": "text_output",
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"showNode": true,
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"type": "LanguageModelComponent"
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},
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"dragging": false,
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"id": "LanguageModelComponent-NSTA6",
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"measured": {
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"height": 534,
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"width": 320
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},
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"position": {
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"x": 2172.8054035815467,
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"y": 413.44064987070726
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},
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"selected": false,
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"type": "genericNode"
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}
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],
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"viewport": {
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"x": -196.5855347896957,
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"y": 78.13813811398654,
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"zoom": 0.5380793988167256
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}
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},
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|
"description": "OpenRAG OpenSearch Nudges generator, based on the OpenSearch documents and the chat history.",
|
|
"endpoint_name": null,
|
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"id": "ebc01d31-1976-46ce-a385-b0240327226c",
|
|
"is_component": false,
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"last_tested_version": "1.6.0",
|
|
"name": "OpenRAG OpenSearch Nudges",
|
|
"tags": [
|
|
"assistants",
|
|
"agents"
|
|
]
|
|
}
|